Structural Complexity and Performance of Support Vector Machines

Kazeem Olorisade, Babatunde; Brereton, Pearl; Andras, Peter

Abstract

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of structural complexity on the performance and statistical reliability of SVMs applied for text mining. We set a theoretical framework for our analysis. We found experimentally that the statistical reliability and performance reduce exponentially with the increase of the structural complexity of the SVMs. This is an important result for the understanding of how the prediction error of SVM predictive data models behaves.

Citation

Kazeem Olorisade, B., Brereton, P., & Andras, P. Structural Complexity and Performance of Support Vector Machines. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 International Joint Conference on Neural Networks (IJCNN)
• Acceptance Date: Jul 18, 2022
• Online Publication Date: Sep 30, 2022
• Publication Date: Sep 30, 2022
• Journal: International Joint Conference on Neural Networks
• Print ISSN: 2161-4393
• Electronic ISSN: 2161-4407
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 2022
• Series Title: 2022 International Joint Conference on Neural Networks (IJCNN)
• DOI: https://doi.org/10.1109/IJCNN55064.2022.9892368
• Keywords: prediction error; statistical reliability; structural complexity; support vector machine; text mining
• Public URL: https://keele-repository.worktribe.com/output/424977
• Publisher URL: https://ieeexplore.ieee.org/document/9892368